Abstract
Vehicle routing problems require efficient computational solutions to reduce operational costs. Therefore, this paper presents a benchmark analysis of Max-Min Ant System (MMAS) combined with local search applied to the Asymmetric and Dynamic Travelling Salesman Problem with Moving Vehicle (ADTSPMV). Different from the well known ADTSP, in the moving vehicle scenario the optimization algorithm continues to improve the TSP solution while the vehicle is visiting the clients. The challenge of this scenario is mainly concerned with the fulfilment of hard time restrictions. In this study we evaluate how MMAS performs combined with US local search, 3-opt local search, and a memory mechanism. Besides that, we demonstrate how to model the moving vehicle restrictions under the MMAS algorithm. To perform the benchmark analysis instances from TSBLIB were selected. The dynamism was emulated by means of changes in traffic factors. The results indicate that for ADTSP the MMAS-US is the best algorithm while for ADTSPMV the MMAS-3opt is the most suitable.
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References
Ant Colony Optimization. http://www.aco-metaheuristic.org/. Accessed 8 Feb 2019
Braekers, K., Ramaekers, K., Van Nieuwenhuyse, I.: The vehicle routing problem: state of the art classification and review. Comput. Ind. Eng. 99, 300–313 (2016)
Chowdhury, S., Marufuzzaman, M., Tunc, H., Bian, L., Bullington, W.: A modified ant colony optimization algorithm to solve a dynamic traveling salesman problem: a case study with drones for wildlife surveillance. J. Comput. Des. Eng. 6, 368–386 (2018)
Cormen, T.: Introduction to Algorithms. MIT Press, Cambridge (2009)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 26, 29–41 (1996)
Gendreau, M., Hertz, A., Laporte, G.: New insertion and postoptimization procedures for the traveling salesman problem. Oper. Res. 40, 1086–1094 (1992)
Johnson, D., Gutin, G., McGeoch, L., Yeo, A., Zhang, W., Zverovitch, A.: Experimental analysis of heuristics for the ATSP. In: Gutin, G., Punnen, A.P. (eds.) The Traveling Salesman Problem and Its Variations, Combinatorial Optimization. Combinatorial Optimization, vol. 12, pp. 445–487. Springer, Boston (2007). https://doi.org/10.1007/0-306-48213-4_10
Jonker, R., Volgenant, T.: Transforming asymmetric into symmetric traveling salesman problems. Oper. Res. Lett. 2, 161–163 (1983)
Larsen, A.: The dynamic vehicle routing problem. Ph.D. thesis, Technical University of Denmark, Kongens, Lyngby, Denmark (2000)
Lin, S., Kernighan, B.: An effective heuristic algorithm for the traveling-salesman problem. Oper. Res. 21, 498–516 (1973)
Mavrovouniotis, M., Muller, F., Yang, S.: Ant colony optimization with local search for dynamic traveling salesman problems. IEEE Trans. Cybern. 47, 1743–1756 (2017)
Mavrovouniotis, M., Yang, S.: Memory-based immigrants for ant colony optimization in changing environments. In: Di Chio, C., et al. (eds.) EvoApplications 2011. LNCS, vol. 6624, pp. 324–333. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20525-5_33
Mavrovouniotis, M., Yang, S.: Interactive and non-interactive hybrid immigrants schemes for ant algorithms in dynamic environments. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1542–1549 (2014)
Mavrovouniotis, M., Yang, S.: Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors. Appl. Soft Comput. 13, 4023–4037 (2013)
Mavrovouniotis, M., Yang, S.: Ant colony optimization with immigrants schemes in dynamic environments. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6239, pp. 371–380. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15871-1_38
Parpinelli, R.S., Plichoski, G.F., Silva, R., Narloch, P.H.: A review of techniques for on-line control of parameters in swarm intelligence and evolutionary computation algorithms. Int. J. Bio-Inspired Comput. 13(1), 1–20 (2019)
Pillac, V., Gendreau, M., Guéret, C., Medaglia, A.: A review of dynamic vehicle routing problems. Eur. J. Oper. Res. 225, 1–11 (2013)
Pedro Schmitt, J., Baldo, F., Stubs Parpinelli, R.: A MAX-MIN ant system with short-term memory applied to the dynamic and asymmetric traveling salesman problem. In: 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pp. 1–6 (2018)
Stützle, T., Hoos, H.: Max-min - ant system. Future Gener. Comput. Syst. 16, 889–914 (2000)
Stutzle, T., Hoos, H.: MAX-MIN ant system and local search for the traveling salesman problem. In: Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC 1997), pp. 309–314 (1997)
TSPLIB. https://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/. Accessed 8 Feb 2019
Wu, Y., Weise, T., Chiong, R.: Local search for the traveling salesman problem: a comparative study. In: 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICC&ICC), pp. 213–220 (2015)
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Schmitt, J.P., Parpinelli, R.S., Baldo, F. (2019). Analysis of Max-Min Ant System with Local Search Applied to the Asymmetric and Dynamic Travelling Salesman Problem with Moving Vehicle. In: Kotsireas, I., Pardalos, P., Parsopoulos, K., Souravlias, D., Tsokas, A. (eds) Analysis of Experimental Algorithms. SEA 2019. Lecture Notes in Computer Science(), vol 11544. Springer, Cham. https://doi.org/10.1007/978-3-030-34029-2_14
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